Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm

Abstract

The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are signifcant to prevent the coronavirus’s spread and ensure more efective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more efective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework’s hyperparameters are tuned by means of a genetic algorithm. The efectiveness of the proposed framework has been examined utilizing a public dataset including 18 diferent blood fndings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates.